A compact deep learning model with 6 convolutional layers and a dense layer was used. Each block of two convolutional layers of size 16, 32, and 64 was followed by a dropout of 0.2 while the dense layer of 512 neurons was followed by a dropout of 0.3. Initially, it was trained on the UCSD dataset [1]} consisting of 85k images. Since the dataset was large and had low noise the model had very low uncertainty in most cases. Then it was trained on the smaller OCTID dataset which had fewer images and five classes covering multiple retinal diseases simulating a clinical setting. The weights from the model trained on the UCSD dataset were finetuned for OCTID using transfer learning, similar to that used in [2]}.
